import argparse
import json
from pathlib import Path
LAUNCH_CODE = """\
import os
import subprocess
import time
import webbrowser
import traceback
# --- CONFIGURATION (配置) ---
CONDA_ENV_NAME = "boviz-dev"
JUPYTER_PORT = 8888
CONDA_BASE_PATH = "/home/qianbo/miniforge"
# --- END CONFIGURATION (配置结束) ---
def main():
print("==================================================")
print(" WSL Jupyter Lab Launcher (Python v6)")
print("==================================================")
WIN_PATH = os.path.dirname(os.path.abspath(__file__))
print(f"[1] Windows Project Path: {WIN_PATH}")
print("[2] Converting to WSL path...")
# --- FIX v6 (最终修复) ---
# 我们放弃使用参数列表，因为 Python 正在破坏反斜杠。
# 我们构建一个完整的命令字符串，并使用 "shell=True"。
# 这会强制 Python 通过 cmd.exe 来运行此命令，
# 就像我们那个能工作的 .bat 脚本一样。
wslpath_command = f'wsl.exe wslpath -u "{WIN_PATH}"'
result = subprocess.run(
wslpath_command, # <-- 修复1: 传入完整命令字符串
shell=True, # <-- 修复2: 使用 shell=True
capture_output=True
# check=True 仍然保持移除
)
# 保持 v3 的健壮的解码逻辑
WSL_PATH = result.stdout.decode('utf-8', errors='replace').strip()
# 保持 v4 的手动检查
if not WSL_PATH.startswith('/mnt/'):
print("\\n❌❌❌ CRITICAL ERROR ❌❌❌")
print("Failed to convert Windows path to WSL path.")
print("wslpath did not return a valid path.")
print("\\n--- COMMAND (Diagnostic) ---")
print(wslpath_command)
print("\\n--- STDOUT (Diagnostic) ---")
print(result.stdout.decode('utf-8', errors='replace'))
print("\\n--- STDERR (Diagnostic) ---")
print(result.stderr.decode('utf-8', errors='replace'))
raise ValueError(f"wslpath failed. Got: {WSL_PATH}")
# --- END FIX (修复结束) ---
print(f" {WSL_PATH}")
# 3. 准备在 WSL 中运行的完整命令
print("[3] Preparing WSL command...")
CONDA_SH_PATH = f"{CONDA_BASE_PATH}/etc/profile.d/conda.sh"
WSL_COMMAND = (
f'source "{CONDA_SH_PATH}" && '
f'conda activate "{CONDA_ENV_NAME}" && '
f"jupyter lab --no-browser --port={JUPYTER_PORT} "
f'--notebook-dir="{WSL_PATH}" --ip=0.0.0.0'
f"--ServerApp.token='' --ServerApp.password=''"
)
# 4. 启动 Jupyter 服务器窗口
print("[4] Starting Jupyter Server in new window...")
server_process = subprocess.Popen(
['cmd.exe', '/k', 'wsl.exe', 'bash', '-l', '-c', WSL_COMMAND],
creationflags=subprocess.CREATE_NEW_CONSOLE
)
# 5. 等待服务器初始化
print("[5] Waiting 5 seconds for server to start...")
time.sleep(5)
# 6. 打开浏览器
print("[6] Opening browser...")
BROWSER_URL = f"http://localhost:{JUPYTER_PORT}/lab"
webbrowser.open(BROWSER_URL)
print("\\n==================================================")
print(" SUCCESS! Server is launching.")
print(" This launcher window will now close.")
print("==================================================")
time.sleep(2)
if __name__ == "__main__":
try:
main()
except Exception as e:
print("\\n❌❌❌ AN ERROR OCCURRED! ❌❌❌")
print(f"Error: {e}")
print("\\n--- Traceback ---")
traceback.print_exc()
print("\\nThis window will pause so you can read the error.")
os.system("pause")
"""
JUPYTER_CODE= {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Boviz 绘图模板\n",
"\n",
"### 1. 导入库并设置路径"
]
},
{
"cell_type": "code",
"execution_count": None,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"from boviz import *\n",
"\n",
"# 在 Notebook 中，我们使用 os.getcwd() 来获取当前目录\n",
"base_dir = os.getcwd()\n",
"csv_path = os.path.join(base_dir, 'data', 'example.csv')\n",
"# exodus_path = os.path.join(base_dir, \"data/test_two_particle_viscos_sintering.e\")\n",
"\n",
"print(f\"Base directory: {base_dir}\")\n",
"print(f\"CSV path: {csv_path}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. 绘制初始粒子分布示意图"
]
},
{
"cell_type": "code",
"execution_count": None,
"metadata": {},
"outputs": [],
"source": [
"plot_initial_particle_schematic(\n",
" coordinates=[[90, 90], [150, 90]],\n",
" radii=[30, 30],\n",
" domain=[240, 180],\n",
" title=\"Initial Particle Distribution\",\n",
" show=True, # 修改为 True 以在 notebook 中显示\n",
" save=True\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. 多曲线对比：不同实验和模拟条件下的收缩率对比"
]
},
{
"cell_type": "code",
"execution_count": None,
"metadata": {},
"outputs": [],
"source": [
"plot_curves_csv(\n",
" path=[csv_path, csv_path, csv_path, csv_path],\n",
" label=[\"Exp 800K\", \"Exp 900K\", \"Sim 800K\", \"Sim 900K\"],\n",
" x=[0, 0, 0, 0],\n",
" y=[1, 2, 3, 4],\n",
" xy_label=[\"Time (s)\", \"Shrinkage Ratio\"],\n",
" title_figure=\"Shrinkage Comparison at Two Temperatures\",\n",
" use_marker=[True, True, False, False],\n",
" legend_ncol=2,\n",
" save=True,\n",
" show=True # 修改为 True\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. 单曲线绘图：绘制单条模拟曲线"
]
},
{
"cell_type": "code",
"execution_count": None,
"metadata": {},
"outputs": [],
"source": [
"plot_curves_csv(\n",
" path=[csv_path],\n",
" label=[\"Sim 800K\"],\n",
" x=[0],\n",
" y=[3],\n",
" title_figure=\"Shrinkage at 800K\",\n",
" save=True,\n",
" show=True # 修改为 True\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5. 样式演示：展示不同颜色、marker、线型等样式"
]
},
{
"cell_type": "code",
"execution_count": None,
"metadata": {},
"outputs": [],
"source": [
"plot_curves_csv(\n",
" path=[csv_path, csv_path],\n",
" label=[\"Exp 800K\", \"Exp 900K\"],\n",
" x=[0, 0],\n",
" y=[1, 2],\n",
" xy_label=[\"Time (s)\", \"Shrinkage Ratio\"],\n",
" use_marker=[True, True],\n",
" title_figure=\"Style Demo\",\n",
" save=True,\n",
" show=True # 修改为 True\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 6. 残差分析图：展示两条曲线的残差"
]
},
{
"cell_type": "code",
"execution_count": None,
"metadata": {},
"outputs": [],
"source": [
"plot_curves_csv(\n",
" path=[csv_path, csv_path],\n",
" label=[\"Sim 800K\", \"Sim 900K\"],\n",
" x=[0, 0],\n",
" y=[3, 4],\n",
" xy_label=[\"Time (s)\", \"Shrinkage Ratio\"],\n",
" title_figure=\"Residual Analysis\",\n",
" show=True, # 修改为 True\n",
" save=True,\n",
" show_residual=True\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 7. 绘制热图示例\n",
"\n",
"*(取消注释以运行)*"
]
},
{
"cell_type": "code",
"execution_count": None,
"metadata": {},
"outputs": [],
"source": [
"# (请记得取消第一个单元格中 exodus_path 的注释)\n",
"\n",
"# plot_heatmap_exodus2d(\n",
"# path=exodus_path,\n",
"# variable=\"Real_Pressure\",\n",
"# time_step=5,\n",
"# cmap='jet',\n",
"# save=True,\n",
"# show=True,\n",
"# font_style='sans',\n",
"# show_ticks=False\n",
"# )"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
TEMPLATE_CODE = '''\
import os
import numpy as np
from boviz import *
base_dir = os.path.dirname(os.path.abspath(__file__))
csv_path = os.path.join(base_dir, 'data', 'example.csv')
# exodus_path = os.path.join(base_dir, "data/test_two_particle_viscos_sintering.e")
#1. 绘制初始粒子分布示意图
plot_initial_particle_schematic(
coordinates=[[90, 90], [150, 90]],
radii=[30, 30],
domain=[240, 180],
title="Initial Particle Distribution",
show=False,
save=True
)
# 2. 多曲线对比：不同实验和模拟条件下的收缩率对比
plot_curves_csv(
path=[csv_path, csv_path, csv_path, csv_path],
label=["Exp 800K", "Exp 900K", "Sim 800K", "Sim 900K"],
x=[0, 0, 0, 0],
y=[1, 2, 3, 4],
xy_label=["Time (s)", "Shrinkage Ratio"],
title_figure="Shrinkage Comparison at Two Temperatures",
use_marker=[True, True, False, False],
legend_ncol=2,
save=True,
show=False
)
# 3. 单曲线绘图：绘制单条模拟曲线
plot_curves_csv(
path=[csv_path],
label=["Sim 800K"],
x=[0],
y=[3],
title_figure="Shrinkage at 800K",
save=True,
show=False
)
# 4. 样式演示：展示不同颜色、marker、线型等样式
plot_curves_csv(
path=[csv_path, csv_path],
label=["Exp 800K", "Exp 900K"],
x=[0, 0],
y=[1, 2],
xy_label=["Time (s)", "Shrinkage Ratio"],
use_marker=[True, True],
title_figure="Style Demo",
save=True,
show=False
)
# 5. 残差分析图：展示两条曲线的残差
plot_curves_csv(
path=[csv_path, csv_path],
label=["Sim 800K", "Sim 900K"],
x=[0, 0],
y=[3, 4],
xy_label=["Time (s)", "Shrinkage Ratio"],
title_figure="Residual Analysis",
show=False,
save=True,
show_residual=True
)
# 6. 直接传入数据进行绘图
x = np.linspace(0, 4*np.pi, 200)
y = np.sin(x)
plot_curves(
data=[(x, y)],
label=[r"$\\sin(x)$"],
xy_label=(r"$x$", r"$\\sin(x)$"),
title_figure="Sine Wave Example",
save=True,
show=False
)
# # 7. 绘制热图示例
# plot_heatmap_exodus2d(
# path=exodus_path,
# variable="Real_Pressure",
# time_step=5,
# cmap='jet',
# save=True,
# show=True,
# font_style='sans',
# show_ticks=False
# )
'''
EXAMPLE_CSV = '''\
Time,Shrinkage_A,Shrinkage_B,Shrinkage_C,Radius_A,Radius_B,Residual_A,Residual_B
0.0,1.0209447982777509,1.0,1.022544492624045,1.0,1.0,0.09145300179805697,0.11871277304679126
0.05025125628140704,0.93078700119278,0.9950230947479447,0.850618700274092,0.9852911718279044,0.9551664160337779,0.03734231526877581,0.05188018637730255
0.10050251256281408,0.9337694925771136,0.9901335064388825,1.0672554599457287,0.9712699284723975,0.9365957375051759,0.0018022256126726242,0.03991562571165031
0.15075376884422112,0.9124100769886012,0.9853175857320949,0.9646029534111357,0.9578748463510758,0.9223459546850981,0.013251253394357189,0.04799346557144779
0.20100502512562815,0.9395107513382187,0.9805617572379888,0.9646678928631606,0.9450523818378945,0.9103328320675559,0.027040150820159755,0.013400072751493028
0.2512562814070352,0.918517171991616,0.9758525511919605,0.8330566604083536,0.9327555784779354,0.8997490585765828,0.04687877579412846,0.07079871530129744
0.30150753768844224,0.8792210653099232,0.9711766348875863,1.0178464777963514,0.9209430289074864,0.8901805959425307,0.05115864797784769,0.01637864646243115
0.35175879396984927,0.9465036609532331,0.966520843789521,0.9167977808873513,0.909578033536796,0.8813814864416436,0.03915920900295252,0.02165595915130574
0.4020100502512563,0.8648709552514344,0.9618722122472979,0.7468935697743287,0.898627912870938,0.8731914750103518,0.0015216709695828187,0.16149898160677276
0.45226130653266333,0.7297325631564083,0.9572180037322346,0.7770582968531939,0.8880634409836181,0.8654992481013339,0.014842237638382058,0.08999962204482043
0.5025125628140704,0.8385407139823808,0.952545740520853,0.8857389370870143,0.8778583754001418,0.8582237589983328,0.02046920010532116,0.11684521529429073
0.5527638190954774,0.8618799267031556,0.947843232749624,0.9049301929477894,0.8679890643480241,0.8513038239771477,0.09879875311791815,0.009929764081485224
0.6030150753768845,0.8120563476107203,0.9430986067674376,0.9060042435981566,0.8584341165818943,0.8446919093701961,0.018986359993188442,0.01401429853297645
0.6532663316582915,0.8421197200350728,0.9383003327139714,0.7334766122527371,0.8491741221877361,0.8383502141469663,0.114070050811105,0.19307785814648543
0.7035175879396985,0.8199321162854664,0.9334372512540906,0.8347236873679941,0.8401914152032126,0.8322480893772356,0.09373711334555605,0.006485559972505346
0.7537688442211056,0.8434832280195514,0.9284985994005441,0.8222161664438415,0.8314698707566196,0.8263602759480302,0.06641088494373783,0.09481741774573418
0.8040201005025126,0.7091516423587425,0.9234740353595257,0.8887021066846745,0.822994730870864,0.8206656641351119,0.0074426369810544615,0.10809377189368738
0.8542713567839196,0.8144569222301328,0.9183536623361391,0.776692338404998,0.8147524542051896,0.8151463977322682,0.03508069652592302,0.073705638590358
0.9045226130653267,0.8968948831829472,0.9131280512394333,0.7301604661839558,0.8067305858926198,0.8097872125155275,0.01700055455711631,0.029023520822439725
0.9547738693467337,0.7522909859589794,0.907788262229454,0.6981179559511411,0.7989176443318138,0.8045749382144927,0.04922967894025382,0.011753916404921183
1.0050251256281408,0.6919002519040867,0.9023258650516879,0.8439697017018186,0.7913030223503886,0.7994981171531658,0.02360888478788168,0.1486274798146839
1.0552763819095479,0.7576304833084876,0.8967329581073316,0.8055380258575947,0.7838769006045072,0.794546707798629,0.018984659599647014,0.03866413348372867
1.105527638190955,0.7879593958948011,0.8910021862110196,0.8034090356309292,0.7766301714407065,0.7897118511954652,0.015154656406218981,0.1731722575973963
1.155778894472362,0.6452574781504304,0.8851267569909556,0.7400612495142068,0.7695543717389426,0.7849856847117047,0.06297819996911212,0.013240218568550444
1.206030150753769,0.5869490048614198,0.8791004558898244,0.7405120288851479,0.7626416234947964,0.7803611918850615,0.04520567816145864,0.03045080451141218
1.256281407035176,0.6742331720605388,0.8729176597283959,0.5880146281850022,0.7558845810946715,0.7758320801688898,0.042633534039092126,0.15175743435602576
1.306532663316583,0.6343243696465698,0.8665733487973595,0.8053731494888088,0.7492763843991841,0.7713926804919332,0.03368916252678005,0.0035781363245920464
1.35678391959799,0.6386333140263972,0.8600631174466541,0.6158615015309037,0.7428106168834638,0.7670378640552942,0.0450828211262279,0.051228660246413975
1.407035175879397,0.5825874070448445,0.8533831831453447,0.6614376191149248,0.7364812681940731,0.7627629728832873,0.10297633293217337,0.0008451363498873521
1.4572864321608041,0.6621401144672502,0.8465303939889643,0.689977285080868,0.7302827005748862,0.7585637614473913,0.01729401759866454,0.047783871307394386
1.5075376884422111,0.6766371306335031,0.8395022346351646,0.5928612953156425,0.724209618691892,0.7544363472789826,0.061042281871111416,0.08138119124950707
1.5577889447236182,0.6127298923812176,0.8322968306524806,0.8001731493389628,0.7182570424521909,0.7503771689349215,0.02719961686315634,0.06959620893315555
1.6080402010050252,0.6088321318668333,0.8249129512710338,0.6274251391829577,0.7124202824675812,0.7463829500207034,0.038117151660807466,0.11310788226081876
1.6582914572864322,0.7081434077229812,0.8173500105280241,0.6067908972831708,0.7066949178598578,0.7424506682372146,0.014688723010190863,0.0056638867980286466
1.7085427135678393,0.5631089378955199,0.809608066804923,0.64058553168321,0.7010767761446486,0.7385775286194518,0.01838870232737677,0.03669615146204832
1.7587939698492463,0.667627256495273,0.8016878207573402,0.46705999308017854,0.6955619149644885,0.7347609402935347,0.003589484761932637,0.013494287989343797
1.8090452261306533,0.6098397212696849,0.7935906116425939,0.6639772957844278,0.690146605470818,0.7309984962026678,0.04789697684585741,0.06333996306365457
1.8592964824120604,0.4933066288247342,0.7853184120540673,0.628306763724403,0.6848273171794484,0.7272879553512855,0.003413449956965813,0.01370299766556954
1.9095477386934674,0.5869091567682225,0.7768738210754518,0.6307383248838679,0.6796007041454492,0.7236272271953355,0.005685354832301275,0.01620005097939397
1.9597989949748744,0.5745421073016223,0.7682600558719739,0.6341535915207429,0.6744635923218718,0.7200143578699169,0.041242833736057916,0.0008390698571727638
2.0100502512562817,0.5364000668608677,0.7594809417396463,0.48842299103788384,0.669412967982713,0.7164475179966656,0.0070512918284863935,0.031115177634543564
2.0603015075376887,0.5164709901922427,0.7505409006374838,0.5639972614372138,0.6644459671043785,0.712924991854903,0.0026551687292387977,0.04686958768631601
2.1105527638190957,0.544011767148791,0.7414449382314499,0.6860908894250208,0.6595598656119689,0.7094451677346187,0.02966127523742708,0.039651092346787244
2.1608040201005028,0.4989536226946192,0.7321986294826655,0.6284932321872049,0.6547520704072156,0.7060065293173672,0.0028509515762108976,0.11988387173616225
2.21105527638191,0.46734845101030037,0.7228081028160838,0.5703964645669753,0.6500201111040873,0.7026076479542953,0.04911894298411106,0.061159444347240674
2.261306532663317,0.6482786392103428,0.7132800229094233,0.5281088965394642,0.6453616324061207,0.6992471757297487,0.11193469648515511,0.012701128352875186
2.311557788944724,0.4574703999444425,0.7036215721456343,0.5083209896939455,0.6407743870665956,0.6959238392149281,0.026104007393431085,0.023006874815430588
2.361809045226131,0.4870642251680577,0.6938404307755529,0.5636453980809554,0.6362562293788792,0.6926364338295035,0.05514027102087267,0.0856938249277266
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10.0,0.12994086212047606,0.08093317214767573,0.07427988631504226,0.28063141816048887,0.367544467966324,0.044903461097547934,0.03585440361794555
'''
[文档]
def init_project(project_name: str):
project_path = Path(project_name)
if project_path.exists():
print(f"❌ 文件夹 {project_name} 已存在。")
return
# 创建目录
(project_path / 'data').mkdir(parents=True)
# 写入模板文件
(project_path / 'start_jupyter.py').write_text(LAUNCH_CODE, encoding='utf-8')
(project_path / 'plot.py').write_text(TEMPLATE_CODE, encoding='utf-8')
(project_path / 'data' / 'example.csv').write_text(EXAMPLE_CSV, encoding='utf-8')
try:
# <--- 修改 4: 使用 json.dumps 将 *字典* 转换为 *字符串* ---
notebook_content = json.dumps(JUPYTER_CODE, indent=1, ensure_ascii=False)
(project_path / 'plot.ipynb').write_text(notebook_content, encoding='utf-8')
except Exception as e:
print(f"❌ 写入 'plot.ipynb' 时出错: {e}")
print(f"--- 成功创建绘图项目：{project_name} ---")
[文档]
def main():
parser = argparse.ArgumentParser(description='boviz CLI 工具')
parser.add_argument('command', choices=['init'], help='可用命令：init')
parser.add_argument('project_name', help='要创建的项目名称')
args = parser.parse_args()
if args.command == 'init':
init_project(args.project_name)
if __name__ == '__main__':
main()